Alifiah, Nur and Kurniasari, Dian and Amanto, Amanto and Warsono, Warsono (2023) Prediction of COVID-19 Using the Artificial Neural Network (ANN) with K-Fold Cross-Validation. Journal ofInformation Systems Engineeringand Business Intelligence, 9 (1). pp. 16-27. ISSN 2443-2555
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Abstract
Background: COVID-19 is a disease that attacks the respiratory system and is highly contagious, so cases of the spread of COVID-19 are increasing every day. The increase in COVID-19 cases cannot be predicted accurately, resulting in a shortage of services, facilities and medical personnel. This number will always increase if the community is not vigilant and actively reduces the rate of adding confirmed cases. Therefore, public awareness and vigilance need to be increased by presenting information on predictions of confirmed cases, recovered cases, and cases of death of COVID-19 so that it can be used as a reference for the government in taking and establishing a policy to overcome the spread of COVID-19.Objective: This research predicts COVID-19 in confirmed cases, recovered cases, and death cases in Lampung ProvinceMethod: This study uses the ANN method to determine the best network architecture for predicting confirmed cases, recovered cases, and deaths from COVID-19 using the k-fold cross-validation method to measure predictive model performance.Results: The method used has a good predictive ability with an accuracy value of 98.22% for confirmed cases, 98.08% for cured cases, and 99.05% for death cases.Conclusion: The ANN method with k-fold cross-validation to predict confirmed cases, recovered cases, and COVID-19 deaths in Lampung Province decreased from October 27,2021,to January 24,2022.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Matematika |
| Depositing User: | DIAN KURNIASARI |
| Date Deposited: | 02 May 2023 01:38 |
| Last Modified: | 02 May 2023 01:38 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/50832 |
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